# TensorFlow NLP Modelling Toolkit

This codebase provides a Natrual Language Processing modeling toolkit written in
[TF2](https://www.tensorflow.org/guide/effective_tf2). It allows researchers and
developers to reproduce state-of-the-art model results and train custom models
to experiment new research ideas.

## Features

* Reusable and modularized modeling building blocks
* State-of-the-art reproducible
* Easy to customize and extend
* End-to-end training
* Distributed trainable on both GPUs and TPUs

## Major components

### Libraries

We provide modeling library to allow users to train custom models for new
research ideas. Detailed intructions can be found in READMEs in each folder.

* [modeling/](modeling): modeling library that provides building blocks (e.g., Layers, Networks, and Models) that can be assembled into transformer-based achitectures .
* [data/](data): binaries and utils for input preprocessing, tokenization, etc.

### State-of-the-Art models and examples

We provide SoTA model implementations, pre-trained models, training and
evaluation examples, and command lines. Detail instructions can be found in the
READMEs for specific papers.

1. [BERT](bert): [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Devlin et al., 2018
2. [ALBERT](albert): [A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942) by Lan et al., 2019
3. [XLNet](xlnet): [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al., 2019
4. [Transformer for translation](transformer): [Attention Is All You Need](https://arxiv.org/abs/1706.03762) by Vaswani et al., 2017
5. [NHNet](nhnet): [Generating Representative Headlines for News Stories](https://arxiv.org/abs/2001.09386) by Gu et al, 2020